Dissection of genomic correlation matrices of US Holsteins using multivariate factor analysis

Study of the Fatty Acid Profile of Milk in Different Sheep Breeds: Evaluation by Multivariate Factorial Analysis

Simple Summary

The quality of milk is strongly influenced by its lipid profile. The increase in fats with nutraceutical properties at the expense of those negative for human health, has always been a goal to improve the functional properties of milk. To achieve this goal, it is essential to know the metabolism of the mammary gland and the relationship between the various lipid components. Much is known about bovine milk, while the aspect relating to the sheep species has not been developed. The present work aims to investigate the relationships between the various fatty acids in sheep’s milk through a multivariate approach, which can highlight the mammary role of lipid synthesis.

Abstract

A multivariate analysis was used to investigate the fatty acid (FA) profile in three different Italian sheep breeds: Comisana, Massese, and Sarda. A sample of 852 animals was considered: 118 Massese, 303 Comisana, 431 Sarda. Sarda sheep were divided into two groups, based on their breeding origin (298 and 133 reared in Sardinia and Tuscany, respectively). Sarda sheep, bred both in Sardinia and in Tuscany, were considered in different groups, both because in these two regions most of the sheep of this breed are reared, and because they differ in geographical characteristics and in the farming system. The individual milk FA composition of dairy ewes was analyzed with multivariate factor analysis. The extracted factors were representative of the following eight groups of fatty acids or functions: factor 1 (odd branched fatty acids and long-chain fatty acids), factor 2 (sn3_position), factor 3 (alternative biohydrogenation), factor 4 (SCD_1), factor 5 (SCD_2), factor 6 (SCD_3), factor 7 (fat secretion) and factor 8 (omega-3). A factor analysis suggested the presence of different metabolic pathways for de novo short- and medium-chain fatty acids and Δ9-desaturase products. The ANOVA of factor scores highlighted the significant effects of the breed. The results of the present study showed that breed is an important factor in defining the fatty acid profile of milk, combined with the effect of the diet. Breeds reared in the same farming system (Comisana, Massese and Sarda reared in Tuscany) showed significant differences for all the factors extracted. At the same time, we found differences between the Sarda sheep reared in Sardinia and Tuscany, two different regions of Italy.

Principal component and multivariate factor analysis of detailed sheep milk fatty acid profile

Fatty acid (FA) profile is one of the most important aspects of the nutritional properties of milk. The FA content in milk is affected by several factors such as diet, physiology, environment, and genetics. Recently, principal component analysis (PCA) and multivariate factor analysis (MFA) have been used to summarize the complex correlation pattern of the milk FA profile by extracting a reduced number of new variables. In this work, the milk FA profile of a sample of 993 Sarda breed ewes was analyzed with PCA and MFA to compare the ability of these 2 multivariate statistical techniques in investigating the possible existence of latent substructures, and in studying the influence of physiological and environmental effects on the new extracted variables. Individual scores of PCA and MFA were analyzed with a mixed model that included the fixed effects of parity, days in milking, lambing month, number of lambs born, altitude of flock location, and the random effect of flock nested within altitude. Both techniques detected the same number of latent variables (9) explaining 80% of the total variance. In general, PCA structures were difficult to interpret, with only 4 principal components being associated with a clear meaning. Principal component 1 in particular was the easiest to interpret and agreed with the interpretation of the first factor, with both being associated with the FA of mammary origin. On the other hand, MFA was able to identify a clear structure for all the extracted latent variables, confirming the ability of this technique to group FA according to their function or metabolic origin. Key pathways of the milk FA metabolism were identified as mammary gland de novo synthesis, ruminal biohydrogenation, desaturation performed by stearoyl-coenzyme A desaturase enzyme, and rumen microbial activity, confirming previous findings in sheep and in other species. In general, the new extracted variables were mainly affected by physiological factors as days in milk, parity, and lambing month; the number of lambs born had no effect on the new variables, and altitude influenced only one principal component and factor. Both techniques were able to summarize a larger amount of the original variance into a reduced number of variables. Moreover, factor analysis confirmed its ability to identify latent common factors clearly related to FA metabolic pathways.

Use of multivariate factor analysis of detailed milk fatty acid profile to perform a genome-wide association study in Italian Simmental and Italian Holstein

Milk fatty acid (FA) profile is a clear example of complex and multiple correlated traits whose genetic basis is difficult to assess. Although genome-wide association (GWA) studies have been successful in the identification of significant genetic variants for complex traits, when correlated phenotypes are analysed separately, the outcomes are difficult to compare and interpret in a metabolic context. Here, we performed a multivariate factor analysis (MFA) on Italian Simmental and Italian Holstein milk fat profiles to extract latent unobserved factors able to explain correlation structure and common metabolic function among different FAs. Individual factor scores obtained by MFA were used to perform a single-SNP based GWA. In both breeds, MFA was able to extract ten latent factors with specific biological meaning, notably: de novo synthesis, desaturation activity and biohydrogenation. The GWA result confirmed the increased power of joint association analysis on multiple correlated traits and allowed us to identify major candidate genes with well-documented function consistent with the metabolic classification of factors obtained, such as DGAT1, FASN and SCD.

Multivariate factor analysis of milk fatty acid composition in relation to the somatic cell count of single udder quarters

The present study investigated whether the fatty acid composition of milk changes in relation to an increase in the milk somatic cell count (SCC) of separate udder quarters. We investigated the potential of multivariate factor analysis to extract metabolic evidence from data on the quantity and quality of milk of quarters characterized by different SCC levels. We collected data from individual milk samples taken from single quarters of 49 Italian Holstein cows from the same dairy farm. Factor analysis was carried out on 64 individual fatty acids. In line with a previous study on multivariate factor analysis, a variable was considered to be associated with a specific factor if the absolute value of its correlation with the factor was ≥0.60. Seven factors were extracted that explained the following groups of fatty acids or functions: de novo synthesis, energy balance, uptake of dietary fatty acids, biohydrogenation, short-chain fatty acids, very long chain fatty acids, and odd- and branched-chain fatty acids. An ANOVA of factor scores highlighted the significant effects of the SCC level on de novo fatty acids and biohydrogenation. The de novo fatty acid factor decreased significantly with a high level of SCC, from just 10,000 cells/mL, whereas the biohydrogenation factor showed a significantly higher level in quarters with SCC levels greater than 400,000 cells/mL. This statistical approach enabled us to reduce the number of variables to a few latent factors with biological significance and to represent groups of fatty acids with a common origin and function. Multivariate factor analysis could therefore be key to studying the influence of SCC on the lipid metabolism of single quarters. This approach also demonstrated the metabolic differences between quarters of the same animal showing a different level of SCC.

Genetic evaluation for latent variables derived from factor analysis in broilers

1. The aim of this study was to investigate the associations between several carcass, performance and meat quality traits in broilers through factor analysis and use the latent variables (i.e. factors) as pseudo-phenotypes in genetic evaluations.2. Factors were extracted using the principal components method and varimax rotation algorithm. Genetic parameters were estimated via Bayesian inference under a multiple-trait animal model.3. All factors taken together explained 71% of the original variance of the data. The first factor, denominated as 'weight', was associated with carcass and body weight traits; and the second factor, defined as 'tenderness', represented traits related to water-holding capacity and shear force. The third factor, 'colour', was associated with traits related to meat colour, whereas the fourth, referenced as 'viscera', was related to heart, liver and abdominal fat.4. The four biological factors presented moderate to high heritability (ranging from 0.35 to 0.75), which may confer genetic gains in this population.5. In conclusion, it seems possible to reduce the number of traits in the genetic evaluation of broilers using latent variables derived from factor analysis.

Characterization of protected designation of origin Italian meat products obtained from heavy pigs fed barley-based diets

A study was conducted to evaluate the quality and sensory properties of protected designation of origin (PDO) Parma ham and Piacentina neck obtained from heavy pigs (Italian Duroc × Italian Large White) fed barley-based diets. Four diets were tested: 1) a corn-based diet (control), 2) the control diet with 80% of a normal-amylose hulled barley variety (Cometa), 3) the control diet with 80% of a normal-amylose hulless barley variety (Astartis), and 4) the control diet with 80% of a low-amylose hulless barley variety (Alamo). All the meat products were analyzed for physicochemical and color parameters. The dry-cured hams and necks were also evaluated for sensory properties. The data of physicochemical, color, and sensory parameters were separately analyzed by multivariate factor analysis, and interpretation of each extracted factor was based on specific original variables loading on each one. The meat products obtained from pigs fed the barley-based diets differed from those obtained from the control pigs on the PUFA factors characterized by C18:2-6 and omega-3:omega-6 ratio. In particular, the meat products obtained from pigs fed the barley-based diets had a lower content of C18:2-6 and a higher omega-3:omega-6 ratio ( < 0.05) than the control. In fresh hams, iodine number and SFA (C16:0 and C18:0) in addition to PUFA and omega-3:omega-6 ratio loaded on the PUFA/SFA factor. The fresh hams produced from pigs fed the barley-based diets had subcutaneous fat (SC) with a lower iodine number and a higher SFA level compared with those produced from the control pigs ( < 0.05). A sex effect was measured for PUFA/SFA and oleic acid factors. In particular, the barrow SC had a lower SFA content, higher PUFA and C18:1-9 levels, and a higher iodine number ( < 0.05) than the gilt SC. There were no appreciable differences in the color and sensory properties of meat products obtained from pigs fed the different diets. The hams from barrows differed from those obtained from gilts on the lean properties factor describing properties related to aspect and odor of dry-cured hams. Indeed, the hams from barrows were depreciated compared with the hams from gilts for minor intensity, brightness, and uniformity of the lean, pinkish intermuscular fat and cured odor. In conclusion, barley could be used as a replacement for corn in heavy pig diets for the production of PDO Italian products without negative effects on the physicochemical, color, or sensory characteristics of meat products.

Variances and correlations of milk production, fertility, longevity, and type traits over time in Australian Holstein cattle

When using historical data, it is often assumed that the genetic correlation of the same trait recorded at different time points is reasonably close to 1. However, selection and possible changes in trait definitions means that this may not necessarily be the case. Regularly monitoring genetic parameters over time is important, as changes could reduce the accuracy of genetic evaluations. About 20 yr (1993 to 2012) of data on milk yield as well as functional and type traits from Australian Holstein dairy cattle were analyzed to assess changes in genetic correlations within and among traits over time by considering 2 traits at a time using linear random regression (RR) and multitrait (MT) models. Both residual and genetic variances for milk yield traits and calving interval (CI) increased over time, with the highest increase observed for protein yield. For most type traits some fluctuations over time were noted in both the residual and additive genetic variances. Genetic correlations among survival (i.e., from first to second lactation), milk yield traits, CI, and some type traits varied over time. The genetic correlation of the same trait (e.g., protein yield, fat yield, and some type traits) measured in different years was also less than 1.0 (0.1-0.9), which is likely to be due to selection or changes in trait definitions. Estimates of parameters from the RR model were generally similar to those from MT models that considered the same trait recorded in different year groups as different traits. However, in the case of survival and CI (i.e., lowly heritable traits), the genetic correlations over time obtained from the MT model were lower (0.21 to 0.75) than those from the RR models (0.9-1.0). Genetic correlations of survival with milk, fat, and protein yields declined from ~0.4 to 0.5 at the beginning of the study period (1993/94) to zero or negative at the end (2009/10), whereas the correlation between CI and milk yield became more unfavorable and increased from 0.3 to 0.5 over the same time period. The same pattern was observed for the genetic correlation between survival and CI, which also became more unfavorable over time and increased from 0.67 to 0.87 in absolute value. The genetic correlations of survival with type traits, such as angularity and body depth, decreased from near zero to negative (-0.3 to -0.4). But genetic correlations between pin set and survival showed less variation (0.2-0.3) over time. Similarly the genetic correlation of CI with body depth and angularity became more antagonistic over time. Over time the importance of traits such as milk yield and overall type as criteria for culling decreased, whereas the importance of fertility and possible disease incidence increased, implying that there has been a switch from voluntary to involuntary reasons dominating culling decisions. Changes in genetic correlations of the same trait and among traits over time have important implications on the accuracy of prediction of traits, such as survival and CI, which often rely on other traits as predictors and ultimately on the accuracy of genetic evaluations (traditional and genomic), and also the prediction of response to selection.